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Dear all

In order to test robustness of an image processing algorithm, I want to 
estimate the signal to noise ratio of my images. It concerns a very rough 
approximation since I merely want to take Poisson noise into account, hence 
making abstraction of detector noise, dark noise etc. (at least for now)
I want to be able to compare with synthetic images (originally without 
noise) in which I gradually raise the Poisson noise. When considering the 
original noiseless image as O, the current image as C and i the voxel number 
I approximate the signal-noise ratio in these synthetic images using 
(Manders et al., 1993):

SNR=20log(sqrt(sum(Ci)²/sum(Ci-Oi)²))

Since there is no original image for real images, this formula doesn't 
apply. I have found several definitions of defining SNR, but none of them 
seem to give values that correspond with what I retrieve after using the 
first formula on the synthetic images. The most common way seems to be using 
the standard deviation of a region with 'no' signal to estimate noise and 
using the dynamic range or maximum of the signal. But this seems rather 
arbitrary and dependent on the chosen region. Is there a way of 
approximating the noise and SNR of a single image - so not by recording the 
image several times - without having to select a signal-free ROI and what 
would be the most reliable definition?
Many thanks in advance.

Kind regards,


winnok




______
ir. Winnok De Vos
Research Assistant

dep. Molecular Biotechnology
Faculty of Bioscience Engineering
Ghent University
Coupure links 653
9000 Ghent
Belgium

tel 0032.(0)9.264.59.71
fax 0032.(0)9.264.62.19
www.molecularbiotechnology.ugent.be